Abstract
Selective attention involves the differential processing of different stimuli, and has widespread psychological and neural consequences. Although computational modeling should offer a powerful way of linking observable phenomena at different levels, most work has focused on the relatively narrow issue of constraints on processing resources. By contrast, we consider statistical and informational aspects of selective attention, divorced from resource constraints, which are evident in animal conditioning experiments involving uncertain predictions and unreliable stimuli. Neuromodulatory systems and limbic structures are known to underlie attentional effects in such tasks.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Broadbent, D. Perception and Communication (Pergamon, Elmsford, New York, 1958).
Kahneman, D. Attention and Effort (Prentice-Hall, Englewood Cliffs, New Jersey, 1973).
Bundesen, C. in Converging Operations in the Study of Visual Selective Attention (eds. Kramer, A. F., Coles, M. G. H. & Logan, G. D.) 1– 44 (Am. Psychol. Assoc., Washington, DC, 1996).
Allport, A. in Attention and Performance vol. 14 (eds. Meyer, D. E. & Kornblum, S.) 183–218 (MIT Press, Cambridge, Massachusetts, 1993).
Dayan, P. & Zemel, R. S. Statistical models and sensory attention. Intl. Conf. Artificial Neural Networks ( 1999).
Robbins, T. W. in The Attentive Brain (ed. Parasuraman, R.) 189– 220 (MIT Press, Cambridge, Massachusetts, 1998).
Dickinson, A. Contemporary Animal Learning Theory (Cambridge Univ. Press, Cambridge, 1980).
Mackintosh, N. J. Conditioning and Associative Learning (Oxford Univ. Press, Oxford, 1983).
Grossberg, S. Processing of expected and unexpected events during conditioning and attention: A psychophysiological theory. Psychol. Rev. 89, 529–572 (1982).
Mackintosh, N. J. A theory of attention: Variations in the associability of stimuli with reinforcement . Psychol. Rev. 82, 276– 298 (1975).
Pearce, J. M. & Hall, G. A model for Pavlovian learning: Variation in the effectiveness of conditioned but not unconditioned stimuli. Psychol. Rev. 87, 532–552 (1980).
Kruschke, J. K. Relating Mackintosh's (1975) theory to connectionist models and human categorization. Talk presented at the Eighth Australasian Mathematical Psychology Conference (Perth, Australia, 1997).
Dayan, P. & Long, T. in Advances in Neural Information Processing Systems vol. 10 (eds. Jordan, M. I., Kearns, M. A. & Solla, S. A.) 117–123 (MIT Press, Cambridge, Massachusetts, 1998).
Kakade, S. & Dayan, P. in Advances in Neural Information Processing Systems vol. 12 (eds. Solla, S. A., Leen, T. K. & Muller, K.-R.) 24–30 (MIT Press, Cambridge, Massachusetts, 2000).
Everitt, B. J. & Robbins, T. W. Central cholinergic systems and cognition. Annu. Rev. Psychol. 48, 649–684 (1997).
Holland, P. C. Brain mechanisms for changes in processing of conditioned stimuli in Pavlovian conditioning: Implications for behavior theory. Anim. Learn. Behav. 25, 373–399 ( 1997).
Holland, P. C. & Gallagher, M. Amygdala circuitry in attentional and representational processes. Trends Cogn. Sci. 3, 65–73 (1999 ).
Sutton, R. in Proceedings of the 7th Yale Workshop on Adaptive and Learning Systems 161–166 (Yale University, New Haven, Connecticut, 1992).
Anderson, B. D. O. & Moore, J. B. Optimal Filtering (Prentice-Hall, Englewood Cliffs, New Jersey, 1979 ).
Pearce, J. M., Wilson, P. N. & Kaye, H. The influence of predictive accuracy on serial conditioning in the rat. Q. J. Exp. Psychol. Comp. Physiol. Psychol. 40, 181–198 (1988).
Swan, J. A. & Pearce, J. M. The influence of predictive accuracy on serial autoshaping: Evidence of orienting responses. J. Exp. Psychol. Anim. Behav. Processes 13, 407– 417 (1987).
Swan, J. A. & Pearce, J. M. The orienting response as an index of stimulus associability in rats. J. Exp. Psychol. Anim. Behav. Processes 14, 292–301 ( 1988).
Wilson, P. N., Boumphrey, P. & Pearce, J. M. Restoration of the orienting response to a light by a change in its predictive accuracy. Q. J. Exp. Psychol. 44B, 17–36 (1992).
Miller, R. R. & Matzel, L. D. in Contemporary Learning Theories: Pavlovian Conditioning and the Status of Traditional Learning Theory (eds., Klein, S. B. & Mowrer, R. R.) 61–84 (Erlbaum, Hillsdale, New Jersey, 1989).
Gallistel, C. R. & Gibbon, J. Time, rate, and conditioning. Psychol. Rev. 107, 289– 344 (2000).
Neal, R. M. Bayesian Learning for Neural Networks (Springer, New York, 1996).
Moran J. & Desimone R. Selective attention gates visual processing in the extrastriate cortex. Science 229, 782–784 (1985).
Lu, Z. L. & Dosher, B. A. External noise distinguishes attention mechanisms. Vision Res. 38, 1183– 1198 (1998).
McAdams, C. J. & Maunsell, J. H. R. Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4. J. Neurosci. 19, 431– 441 (1999).
Treue, S. & Martinez Trujillo, J. C. Feature-based attention influences motion processing gain in macaque visual cortex. Nature 399, 575–579 ( 1999).
Cohen, J. D. & Servan-Schreiber, D. A theory of dopamine function and its role in cognitive deficits in schizophrenia. Schizophrenia Bull. 19, 85–104 ( 1993).
Han, J.-S., Holland, P. C. & Gallagher, M. Disconnection of the amygdala central nucleus and substantia innominata/nucleus basalis disrupts increments in conditioned stimulus processing in rats. Behav. Neurosci. 113, 143– 151 (1999).
Corwin, J. V. & Reep, R. L. Rodent posterior parietal cortex as a component of a cortical network mediating directed spatial attention . Psychobiology 26, 87– 102 (1999).
Colby, C. L. & Goldberg, M. E. Space and attention in parietal cortex. Annu. Rev. Neurosci. 22, 319– 349 (1999).
Mesulam, M. M. Spatial attention and neglect: parietal, frontal and cingulate contributions to the mental representation and attentional targeting of salient extrapersonal events. Phil. Trans. R. Soc. Lond. B Biol. Sci. 354 , 1325–1346 (1999).
Lubow, R. E. Latent Inhibition and Conditioned Attention Theory (Cambridge Univ. Press, New York, 1989).
Solomon, P. R. & Moore, J. W. Latent inhibition and stimulus generalization of the classically conditioned nictitating membrane response in rabbits (Oryctolagus cuniculus) following hippocampal ablation . J. Comp. Physiol. Psychol. 89, 1192– 1203 (1975).
Baxter, M. G., Holland, P. C. & Gallagher, M. Disruption of decrements in conditioned stimulus processing by selective removal of hippocampal cholinergic input. J. Neurosci. 17, 5230–5236 ( 1997).
Weiner, I. Neural substrates of latent inhibition: The switching model. Psychol. Bull. 108, 442–461 ( 1990).
Eichenbaum, H. The hippocampus: The shock of the new. Curr. Biol. 9, R482–R484 (1999).
Houk, J. C., Davis, J. L. & Beiser, D. G. (eds.) Models of Information Processing in the Basal Ganglia (MIT Press, Cambridge, Massachusetts, 1995).
Wickens, J. & Kötter, R. in Models of Information Processing in the Basal Ganglia (eds., Houk, J. C., Davis, J. L. & Beiser, D. G.) 187–214 (MIT Press, Cambridge, Massachusetts, 1995).
Kropotov J. D. & Etlinger S. C. Selection of actions in the basal ganglia-thalamocortical circuits: review and model. Int. J. Psychophysiol. 31, 197–217 (1999).
Doya, K. What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? Neural Networks 12, 961– 974 (1999).
Hatfield, T., Han, J.-S., Conley, M., Gallagher, M. & Holland, P. C. Neurotoxic lesions of basolateral, but not central, amygdala interfere with Pavlovian second-order conditioning and reinforcer devaluation effects. J. Neurosci. 16, 5256 –5265 (1996).
Schoenbaum, G., Chiba, A. A. & Gallagher, M. Neural encoding in orbitofrontal cortex and basolateral amygdala during olfactory discrimination learning. J. Neurosci. 19, 1876–1884 ( 1999).
Schultz, W., Tremblay, L. & Hollerman, J. R. Reward processing in primate orbitofrontal cortex and basal ganglia. Cereb. Cortex 10, 272 –283 (2000).
Ballard, D. H. Animate vision. Artificial Intelligence 48, 57–86 (1991).
Schultz, W. & Dickinson, A. Neuronal coding of prediction errors. Annu. Rev. Neurosci. 23, 473– 500 (2000).
Rescorla, R. A. & Wagner, A. R. in Classical Conditioning II: Current Research and Theory (eds., Black, A. H. & Prokasy, W. F.), 64–69 (Appleton-Century-Crofts, New York, 1972).
Sutton, R. S. Learning to predict by the methods of temporal difference. Machine Learning 3, 9–44 ( 1988).
Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 ( 1997).
Bordley, R. F. The combination of forecasts: a Bayesian approach. J. Operational Res. Soc. 33, 171–174 ( 1982).
Lindley, D. V. in Bayesian Statistics 2 (eds., Bernado, J. M., DeGroot, M. M., Lindley, D. V. & Smith, A. F. M.) 375–390 (North Holland, Amsterdam, 1985).
Jacobs, R. A., Jordan, M. I. & Barto, A. G. Task decomposition through competition in a modular connectionist architecture: the what and where vision tasks. Cogn. Sci. 15, 219–250 ( 1991).
Acknowledgements
We are grateful to Nathaniel Daw, Anthony Dickinson, Alex Kacelnik, Theresa Long, Terry Sejnowski, and Rich Sutton for discussions covering many of these issues, and particularly to Nathaniel Daw for comments on an earlier version of this paper. The authors' work is funded by the Gatsby Charitable Foundation (P.D. and S.K.), the National Science Foundation (S.K.) and NIDA (P.R.M.).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dayan, P., Kakade, S. & Montague, P. Learning and selective attention. Nat Neurosci 3 (Suppl 11), 1218–1223 (2000). https://doi.org/10.1038/81504
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1038/81504
This article is cited by
-
Neural and behavioral evidence for oxytocin’s facilitatory effects on learning in volatile and stable environments
Communications Biology (2024)
-
Exploring global trends and future directions in advertising research: A focus on consumer behavior
Current Psychology (2024)
-
Developmental changes in exploration resemble stochastic optimization
Nature Human Behaviour (2023)
-
Neurocomputational mechanism of real-time distributed learning on social networks
Nature Neuroscience (2023)
-
Humans display interindividual differences in the latent mechanisms underlying fear generalization behaviour
Communications Psychology (2023)